{"title":"猪与蜂箱用例分析","authors":"D. Kendal, Oded Koren, N. Perel","doi":"10.14257/IJDTA.2016.9.12.24","DOIUrl":null,"url":null,"abstract":"Corporations are changing their practices to data-driven big data initiatives, as big data analytics has provided companies with the ability to grow their businesses and increase competition. As the importance of data analytics grew, so accordingly did the size of the data to analyze, thus demanding a more powerful data platform. This paper shows a case study of two High Level Query Languages that are constructed on top of Hadoop MapReduce; Pig and Hive. By creating a query in each query language, both resulting in an identical output, and by running each query 30 times on 2 different sized files (120 runs total), this comparison provides a statistically significant conclusion.","PeriodicalId":13926,"journal":{"name":"International journal of database theory and application","volume":"12 1 1","pages":"267-276"},"PeriodicalIF":0.0000,"publicationDate":"2016-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Pig Vs. Hive Use Case Analysis\",\"authors\":\"D. Kendal, Oded Koren, N. Perel\",\"doi\":\"10.14257/IJDTA.2016.9.12.24\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Corporations are changing their practices to data-driven big data initiatives, as big data analytics has provided companies with the ability to grow their businesses and increase competition. As the importance of data analytics grew, so accordingly did the size of the data to analyze, thus demanding a more powerful data platform. This paper shows a case study of two High Level Query Languages that are constructed on top of Hadoop MapReduce; Pig and Hive. By creating a query in each query language, both resulting in an identical output, and by running each query 30 times on 2 different sized files (120 runs total), this comparison provides a statistically significant conclusion.\",\"PeriodicalId\":13926,\"journal\":{\"name\":\"International journal of database theory and application\",\"volume\":\"12 1 1\",\"pages\":\"267-276\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-12-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International journal of database theory and application\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.14257/IJDTA.2016.9.12.24\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International journal of database theory and application","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.14257/IJDTA.2016.9.12.24","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Corporations are changing their practices to data-driven big data initiatives, as big data analytics has provided companies with the ability to grow their businesses and increase competition. As the importance of data analytics grew, so accordingly did the size of the data to analyze, thus demanding a more powerful data platform. This paper shows a case study of two High Level Query Languages that are constructed on top of Hadoop MapReduce; Pig and Hive. By creating a query in each query language, both resulting in an identical output, and by running each query 30 times on 2 different sized files (120 runs total), this comparison provides a statistically significant conclusion.
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